In processing digital signals received from radar arrays in order to detect targets and estimate target angles, one technique is called the maximum likelihood (ML) procedure, or maximum likelihood estimation. In the ML procedure, a search is performed over all possible angular directions of the target, and selecting a direction that yields a set of beam weights that produces the highest probability of target detection. The ML procedure is an alternative to monopulse processing for determining the angular location of a target. In monopulse processing, the angular location of a target is determined by comparing measurements received by two or more simultaneous beams. In a typical digital beamforming radar, one beam is formed in transmission, and two or more beams are formed on reception for angle measurement. These beams correspond to the sum beam, azimuth difference beam, and elevation difference beam. The sum beam is used in target detection. Once a target is detected, the ratio of the azimuth difference beam over the sum beam is used for estimation of the azimuth angle, and the ratio of the elevation difference beam over the sum beam is used for estimation of the elevation angle.
Monopulse processing is subject to beam shape loss, in which the signal to noise ratio is lower, the farther the target is from the boresight. The ML procedure eliminates beam shape loss in the receive mode. As a result, the ML procedure permits searching over a greater volume of space. Also, the ML procedure supports improved accuracy in angle estimation when compared with monopulse processing.
A disadvantage in prior art methods of use of the ML procedure is the high computational complexity involved in the ML procedure. A direct solution for the location of a target in a two-dimensional array using the ML technique is not computationally tractable in view of the calculation of the adaptive weights of the coefficients, and the searching of angles in a two-dimensional space. Since the searching process does not address the problem of jamming, assignment of adaptive weights to the coefficients is required to perform target detection and angle estimation in the presence of jammers. The covariance matrix for the adaptive weight computation requires a sample size on the order of the array size. The computational complexity of the inverse of the covariance matrix is on the order of the cube of the array size. The search for the two-dimensional angle is also computationally intense, as the search requires a search for all positions within the main beam.
One prior art method for overcoming these problems is disclosed in R. M. Davis and R. L. Fante, “A Maximum-Likelihood Beamspace Processor for Improved Search and Track,” IEEE Transactions of Antennas and Propagation, vol. 49, no. 7, July 2001 (“Davis and Fante”). Davis and Fante reduce the computational load required by adaptive processing by performing deterministic beamforming on a small number of beams, such as 4-7 beams. However, this approach does not change the computational burden of the two-dimensional angle search.
It is thus desired to obtain a radar signal processing method and system employing the ML procedure which is not as computationally intensive as prior art methods in at least of process of two-dimensional angle search and calculation of the adaptive weights.